Ollama makes running large language models locally as simple as running a container. Combined with Pixeltable, you can build production-ready AI applications that run entirely on your hardware.
Why Run LLMs Locally with Ollama?#
Ollama is the easiest way to run open-source LLMs locally. It handles model management, optimization, and serving, making local AI accessible to everyone.
When combined with Pixeltable's declarative infrastructure, you get powerful local AI with enterprise-grade orchestration.
Key Benefits#
- Complete Privacy: Your data never leaves your machine
- Zero API Costs: No per-token charges
- Full Control: Choose and customize your models
- Offline Capable: Works without internet connection
Popular Models#
- Llama 3.2: Meta's latest open-weight model (1B, 3B)
- Qwen 2.5: Alibaba's multilingual model
- Mistral: European efficiency champion
- Gemma 2: Google's compact powerhouse
Getting Started#
bash
Basic Chat Completions#
python
Comparing Models#
python
Local Embeddings#
python
Hardware Requirements#
| Model Size | RAM Needed | Best For |
|---|---|---|
| 0.5B - 1B | 2-4GB | Development, simple tasks |
| 3B - 7B | 8-16GB | General use |
| 13B - 70B | 32GB+ | Complex reasoning |
Ollama vs Cloud APIs#
| Consideration | Ollama | Cloud APIs |
|---|---|---|
| Privacy | Complete | Data leaves your network |
| Cost | Hardware only | Per-token pricing |
| Speed | Hardware-dependent | Consistent |
| Model Quality | Open-source | Proprietary (often better) |
